In computing, half precision (sometimes called FP16 or float16) is a binary floatingpoint computer number format that occupies 16 bits (two bytes in modern computers) in computer memory. It is intended for storage of floatingpoint values in applications where higher precision is not essential, in particular image processing and neural networks.
Almost all modern uses follow the IEEE 7542008 standard, where the 16bit base2 format is referred to as binary16, and the exponent uses 5 bits. This can express values in the range ±65,504, with the minimum value above 1 being 1 + 1/1024.
Depending on the computer, halfprecision can be over an order of magnitude faster than double precision, e.g. 550 PFLOPS for halfprecision vs 37 PFLOPS for double precision on one cloud provider.^{[1]}
Floatingpoint formats 

IEEE 754 

Other 
Several earlier 16bit floating point formats have existed including that of Hitachi's HD61810 DSP of 1982 (a 4bit exponent and a 12bit mantissa),^{[2]} Thomas J. Scott's WIF of 1991 (5 exponent bits, 10 mantissa bits)^{[3]} and the 3dfx Voodoo Graphics processor of 1995 (same as Hitachi).^{[4]}
ILM was searching for an image format that could handle a wide dynamic range, but without the hard drive and memory cost of single or double precision floating point.^{[5]} The hardwareaccelerated programmable shading group led by John Airey at SGI (Silicon Graphics) invented the s10e5 data type in 1997 as part of the 'bali' design effort. This is described in a SIGGRAPH 2000 paper^{[6]} (see section 4.3) and further documented in US patent 7518615.^{[7]} It was popularized by its use in the opensource OpenEXR image format.
Nvidia and Microsoft defined the half datatype in the Cg language, released in early 2002, and implemented it in silicon in the GeForce FX, released in late 2002.^{[8]} Since then support for 16bit floating point math in graphics cards has become very common.^{[citation needed]}
The F16C extension in 2012 allows x86 processors to convert halfprecision floats to and from singleprecision floats with a machine instruction.
The IEEE 754 standard^{[9]} specifies a binary16 as having the following format:
The format is laid out as follows:
The format is assumed to have an implicit lead bit with value 1 unless the exponent field is stored with all zeros. Thus, only 10 bits of the significand appear in the memory format but the total precision is 11 bits. In IEEE 754 parlance, there are 10 bits of significand, but there are 11 bits of significand precision (log_{10}(2^{11}) ≈ 3.311 decimal digits, or 4 digits ± slightly less than 5 units in the last place).
The halfprecision binary floatingpoint exponent is encoded using an offsetbinary representation, with the zero offset being 15; also known as exponent bias in the IEEE 754 standard.
Thus, as defined by the offset binary representation, in order to get the true exponent the offset of 15 has to be subtracted from the stored exponent.
The stored exponents 00000_{2} and 11111_{2} are interpreted specially.
Exponent  Significand = zero  Significand ≠ zero  Equation 

00000_{2}  zero, −0  subnormal numbers  (−1)^{signbit} × 2^{−14} × 0.significantbits_{2} 
00001_{2}, ..., 11110_{2}  normalized value  (−1)^{signbit} × 2^{exponent−15} × 1.significantbits_{2}  
11111_{2}  ±infinity  NaN (quiet, signalling) 
The minimum strictly positive (subnormal) value is 2^{−24} ≈ 5.96 × 10^{−8}. The minimum positive normal value is 2^{−14} ≈ 6.10 × 10^{−5}. The maximum representable value is (2−2^{−10}) × 2^{15} = 65504.
These examples are given in bit representation of the floatingpoint value. This includes the sign bit, (biased) exponent, and significand.
Binary  Hex  Value  Notes 

0 00000 0000000000  0000  0  
0 00000 0000000001  0001  2^{−14} × (0 + 1/1024 ) ≈ 0.000000059604645  smallest positive subnormal number 
0 00000 1111111111  03ff  2^{−14} × (0 + 1023/1024 ) ≈ 0.000060975552  largest subnormal number 
0 00001 0000000000  0400  2^{−14} × (1 + 0/1024 ) ≈ 0.00006103515625  smallest positive normal number 
0 01101 0101010101  3555  2^{−2} × (1 + 341/1024 ) ≈ 0.33325195  nearest value to 1/3 
0 01110 1111111111  3bff  2^{−1} × (1 + 1023/1024 ) ≈ 0.99951172  largest number less than one 
0 01111 0000000000  3c00  2^{0} × (1 + 0/1024 ) = 1  one 
0 01111 0000000001  3c01  2^{0} × (1 + 1/1024 ) ≈ 1.00097656  smallest number larger than one 
0 11110 1111111111  7bff  2^{15} × (1 + 1023/1024 ) = 65504  largest normal number 
0 11111 0000000000  7c00  ∞  infinity 
1 00000 0000000000  8000  −0  
1 10000 0000000000  c000  2  
1 11111 0000000000  fc00  −∞  negative infinity 
By default, 1/3 rounds down like for double precision, because of the odd number of bits in the significand. The bits beyond the rounding point are 0101... which is less than 1/2 of a unit in the last place.
Min  Max  interval 

0  2^{−13}  2^{−24} 
2^{−13}  2^{−12}  2^{−23} 
2^{−12}  2^{−11}  2^{−22} 
2^{−11}  2^{−10}  2^{−21} 
2^{−10}  2^{−9}  2^{−20} 
2^{−9}  2^{−8}  2^{−19} 
2^{−8}  2^{−7}  2^{−18} 
2^{−7}  2^{−6}  2^{−17} 
2^{−6}  2^{−5}  2^{−16} 
2^{−5}  2^{−4}  2^{−15} 
2^{−4}  1/8  2^{−14} 
1/8  1/4  2^{−13} 
1/4  1/2  2^{−12} 
1/2  1  2^{−11} 
1  2  2^{−10} 
2  4  2^{−9} 
4  8  2^{−8} 
8  16  2^{−7} 
16  32  2^{−6} 
32  64  2^{−5} 
64  128  2^{−4} 
128  256  1/8 
256  512  1/4 
512  1024  1/2 
1024  2048  1 
2048  4096  2 
4096  8192  4 
8192  16384  8 
16384  32768  16 
32768  65519  32 
65519  ∞  ∞ 
65519 is the largest number that will round to a finite number (65504), 65520 and larger will round to infinity. This is for roundtoeven, other rounding strategies will change this cutoff.
ARM processors support (via a floating point control register bit) an "alternative halfprecision" format, which does away with the special case for an exponent value of 31 (11111_{2}).^{[10]} It is almost identical to the IEEE format, but there is no encoding for infinity or NaNs; instead, an exponent of 31 encodes normalized numbers in the range 65536 to 131008.
This format is used in several computer graphics environments to store pixels, including MATLAB, OpenEXR, JPEG XR, GIMP, OpenGL, Vulkan, Cg, Direct3D, and D3DX. The advantage over 8bit or 16bit integers is that the increased dynamic range allows for more detail to be preserved in highlights and shadows for images, and the linear representation of intensity making calculations easier. The advantage over 32bit singleprecision floating point is that it requires half the storage and bandwidth (at the expense of precision and range).^{[5]}
Hardware and software for machine learning or neural networks tend to use half precision: such applications usually do a large amount of calculation, but don't require a high level of precision.
If the hardware has instructions to compute halfprecision math, it is often faster than single or double precision. If the systems has SIMD instructions that can handle multiple floatingpoint numbers within one instruction, half precision can be twice as fast by operating on twice as many numbers simultaneously.^{[11]}
Several versions of the ARM architecture have support for half precision.^{[12]}
Support for half precision in the x86 instruction set is specified in the AVX512_FP16 instruction set extension implemented in the Intel Sapphire Rapids processor.^{[13]}